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Record W4387411623 · doi:10.1002/aws2.1356

Design considerations for biological ion exchange drinking water filters: Resin selection, backwash, and regenerations

2023· article· en· W4387411623 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAWWA Water Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDissolved organic carbonIon exchangeWater treatmentSulfateFilter (signal processing)Environmental scienceFiltration (mathematics)Ion-exchange resinPilot plantChemistryPulp and paper industryEnvironmental chemistryEnvironmental engineeringIonInorganic chemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Two pilot studies provide insights into the design and operations of biological ion exchange (BIEX) drinking water filters. A lab‐scale pilot with strong‐base anionic (SBA) and weak‐base anionic (WBA) resins demonstrated 35% and 31% removal of dissolved organic carbon (DOC) up to 30 months, until secondary ion exchange exhaustion. While the WBA resins exhibited 14% less removal of DOC, their larger capacity doubled the duration until filter exhaustion. WBA filters were less affected than their SBA counterparts by sulfate‐containing inlet waters. In a second pilot, while water with high DOC yielded rapid exhaustion of SBA resins, air scouring increased the breakup of filter media and improved solids removal by 30× compared to hydraulic backwash alone. Significantly, DOC removal improved by 36% for a week following air scour. Key recommendations include the use of WBA resins to extend operating life while implementing air scouring can dramatically improve short‐term DOC removal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.262
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it